POS1127 USE OF ARTIFICIAL INTELLIGENCE AND CLOUD-BASED INFRASTRUCTURE TO IMPROVE THE SPEED AND ACCURACY OF ELIGIBILITY READS IN OSTEOARTHRITIS TRIALS.
Authors: O. Kubassova, M. Boesen, C. Pereira Da Costa, J. O’lynn, A. Patterson, D. Kessler
Journal: Annals of the Rheumatic Diseases (2022)

Background The Kellgren-Lawrence grading (KLG) system is used in clinical trials of osteoarthritis (OA) to define the structural severity of the disease as part of patient eligibility assessment. However, the use of KLG system has proven to be challenging due to considerable inter-reader variability[1], [2], which may result in recruitment of sub-optimal patient cohort or delays in patient recruitment. Objectives The objective of this study was to determine the impact of an AI-assisted, cloud-based data management system on the rate of adjudication and the speed of OA patient recruitment. Methods A total of 3855 bilateral fixed-flexion posteroanterior radiographs of the tibiofemoral joints from global multi-centre trials were included in this study. Two experienced readers performed an initial KLG assessment of both knees; the adjudication was performed by a third experienced reader. A cloud-based imaging data management system was deployed, the readers could access the data simultaneously and adjudication was automatically triggered. We quantified the adjudication rate and the distribution of disagreements in KLG scores provided by the initial readers. Furthermore, the delay in delivery time of the KLG reports to the recruiting site was recorded. Results 48% (1836) of the initial reads required adjudication. Approximately 70% of the disagreements affected the conventional KLG 2-3 inclusion range of OA clinical trials. Use of the cloud-based data management allowed 41% of the reports to be delivered within 24 hours, if no adjudication was required vs an average of 5 days as estimated based on the readers’ prior experience. Table 1 provides details on the distribution of disagreements resulting in adjudication reads. Figure 1 shows the delivery time for KLG with and without adjudication. Table 1. Distribution of disagreements of initial reads resulting in an adjudication read being triggered. Disagreement triggering Adjudication Number of Cases Percentage KLG 0 – 1 713 27.8% KLG 1 – 2 820 31.9% KLG 2 – 3 482 18.7% KLG 3 – 4 446 17.4% Other 110 4.3% Total 2569 100% Figure 1. Time to deliver eligibility report Nehrer et al. showed that assisting the readers with AI generated KLG scoring reports, the agreement rate between readers for KLG assessment increased by 21% [2]. Adding to this, 30% of the adjudications (stemming from KLG 0 –1 disagreements between readers) could be avoided when using AI generated reports. Conclusion We assess the rate of adjudication and speed of reporting of KLG of data from multi-centre OA clinical trials. Future work is planned to assess the effect of AI-assisted OA grading systems within our cloud-based data management system on reader agreement and recruitment speed in global clinical trials. References [1]D. J. Hunter et al. , “OARSI Clinical Trials Recommendations: Knee imaging in clinical trials inosteoarthritis,” Osteoarthritis and Cartilage , vol. 23, no. 5. W.B. Saunders Ltd, pp. 698–715, 2015. doi: 10.1016/j.joca.2015.03.012. [2]S. Nehrer et al. , “Automated Knee Osteoarthritis Assessment Increases Physicians’ Agreement Rate and Accuracy: Data from the Osteoarthritis Initiative,” Cartilage , vol. 13, no. 1_suppl, pp. 957S-965S, Dec. 2021, doi: 10.1177/1947603519888793. Disclosure of Interests Olga Kubassova Grant/research support from: Takeda, Lilly, Abbvie, Pfizer, Mikael Boesen Speakers bureau: Lilly, Novartis, Abbvie, Pfizer, Cristiano Pereira da Costa: None declared, Julia O’Lynn: None declared, Andrew Patterson: None declared, Dimitri Kessler: None declared